DTU-MLOps
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DTU-MLOps
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Timeplan
S1 - Development Environment 💻
S1 - Development Environment 💻
M1 - The command line
M2 - Package Manager
M3 - Editor
M4 - Deep Learning Software
S2 - Organisation and Version Control 📁
S2 - Organisation and Version Control 📁
M5 - Git
M6 - Code structure
M7 - Good coding practice
M8 - Data version control
S3 - Reproduceability ♻️
S3 - Reproduceability ♻️
M9 - Docker
M10 - Config Files
S4 - Debugging, Profiling and Logging ⏱️
S4 - Debugging, Profiling and Logging ⏱️
M11 - Debugging
M12 - Profiling
M13 - Logging
M14 - Boilerplate
S5 - Continuous Integration ✔️
S5 - Continuous Integration ✔️
M15 - Unittesting
M16 - Github Actions
M17 - Pre commit
M18 - Continuous Containers
M19 - Continuous Machine Learning
S6 - The cloud 🌐
S6 - The cloud 🌐
M20 - Cloud Setup
M21 - Using the Cloud
S7 - Deployment 📦
S7 - Deployment 📦
M22 - Requests and APIs
M23 - Local Deployment
M24 - Cloud Deployment
S8 - Monitoring 📊
S8 - Monitoring 📊
M25 - Data Drifting
M26 - System Monitoring
S9 - Scalable applications ⚖️
S9 - Scalable applications ⚖️
M27 - Distributed Data Loading
M28 - Distributed Training
M29 - Scalable Inference
S10 - Extra 🔥
S10 - Extra 🔥
M30 - Command Line Interfaces
M31 - Documentation
M32 - Hyperparameter optimization
M33 - High Performance Clusters
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